Inconsistency, impracticability and non-standardization of the selection, measurement and reporting of outcomes are three primary existing issues in clinical trials. These problems pose a threat to huge research waste when the results of similar studies are not able to be combined or compared. The key for resolution will be to standardize outcomes in traditional Chinese medicine (TCM) clinical trials and to establish a core outcome set (COS), which is a set of outcomes to be reported as a minimum in all TCM clinical trials of similar healthcare system and syndromes. The first step in the development of COS is to collect all existing outcomes, that is, to build a pool of outcomes for clinical trials of TCM. A pool of outcome is the basis of developing COS, which is important to follow strict and scientific methodology. This paper aims to construct an outcome pool from published literature, clinical trial registration protocols, and clinicians, and patients questionnaires were used to form a list of outcomes. In addition, the influencing factors of constructing an outcome pool and considerations for each problem are summarized in order to provide guidance and reference for the development of COS in clinical trials for TCM.
Based on the imaging photoplethysmography (iPPG) and blind source separation (BSS) theory the author put forward a method for non-contact heartbeat frequency estimation. Using the recorded video images of the human face in the ambient light with Webcam, we detected the human face through software, separated the detected facial image into three channels RGB components. And then preprocesses i.e. normalization, whitening, etc. were carried out to a certain number of RGB data. After the independent component analysis (ICA) theory and joint approximate diagonalization of eigenmatrices (JADE) algorithm were applied, we estimated the frequency of heart rate through spectrum analysis. Taking advantage of the consistency of Bland-Altman theory analysis and the commercial Pulse Oximetry Sensor test results, the root mean square error of the algorithm result was calculated as 2.06 beat/min. It indicated that the algorithm could realize the non-contact measurement of heart rate and lay the foundation for the remote and non-contact measurement of multi-parameter physiological measurements.
As one of the important indexes for the diagnosis and treatment of cardiovascular diseases, cardiac output can reflect the state of cardiovascular system timely, and can play a guiding role in the treatment of related diseases. In recent years detection technology of cardiac output has caused great attention, especially minimally invasive and non-invasive methods. In this paper, the principle of non-invasive detection methods and their recent developments are described, and various detection methods are also analyzed.
Accurate measurements of voltage and current from electrosurgery are the basis of development of electrosurgery with feedback function. We, therefore, developed a parameter measurement system based on PC, with high voltage and current from electrosurgery being sensed with transformers, amplified, filtered, transformed into single-ended signals, and then into RMS signals. The root mean square (RMS) signals were transformed into digital signals through DAQ card and the data was processed in PC with Labview. The process included sampling, displaying and storage. The experiment results indicated that the measurement system could measure the output parameters from electrosurgery steadily and correctly so that the development of the system has been successful. It can be the basis of development of embedded parameters measurement system and can provide accurate feedback information for intellectual electrosurgery.
ObjectiveTo explore the anthropometric changes of the auricle after auricular cartilage unfolding in moderate concha-type microtia patients, so as to provide the basis to help evaluate surgical timing and prognostic.MethodsA total of 33 children with moderate concha-type microtia, who were treated with auricular cartilage unfolding between October 2016 and September 2018 and met the inclusive criteria, were included in the study. There were 24 boys and 9 girls with an average age of 1.4 years (range, 1-3 years). Sixteen cases were left ears and 17 cases were right ears. The follow-up time was 12-23 months (mean, 17.5 months). The affected auricular detailed structures were observed and quantitatively analyzed before operation and at immediate after operation. The width, length, and perimeter of auricle before operation and at immediate after operation and at last follow-up were noted with three dimensional-scanning technology. The normal auricle was noted as control.ResultsThere were (7.5±1.0) and (11.3±0.8) structures of the affected auricle at pre- and post-operation, respectively, showing significant difference between pre- and post-operation (t=23.279, P=0.000). The length, width, and perimeter of the affected auricle constantly increased after operation, and there were significant differences between pre-operation and immediately after operation and between immediately after operation and last follow-up (P<0.05). The differences of length, width, and perimeter of the affected auricle between immediately after operation and last follow-up were (3.13±1.44), (2.44±0.92), and (8.50±3.76) mm, respectively. And the differences of length, width, and perimeter of the normal auricle between pre-operation and last follow-up were (3.16±1.54), (2.35±0.86), and (9.79±4.60) mm, respectively. There was no significant difference in the differences of length, width, and perimeter between the affected auricle and the normal auricle (P>0.05).ConclusionThe auricular cartilage unfolding in treatment of the moderate concha-type microtia can receive more ear structures and increase auricle sizes, which make it possible for free composite tissue transplantation. In addition, the affected and the contralateral normal auricles have a very similar growth rate and it offers the theoretical foundation for the early treatment for moderate concha-type microtia.
Objective To investigate the application of magnetic resonance imaging (MRI) in preoperative assessment of rectal cancer. Methods Combined with the literatures, the MRI features and measurements of rectal tumor staging, extramural vascular invasion, circumferential margin involvement, and the distance between distal margin of the tumor from the anorectal ring and the anal margin were described. Results On T2-weighted images (T2WI), T1 staging-tumors were those in which the normal submucosa was replaced by the iso-intensity of tumor tissue without invasion of muscularis propria; T2 staging-tumors were those with extension into the muscularis propria, but not invaded the high-intensity of mesorectal fat; T3 staging-tumors manifested as the rectal tumor penetrated into the muscularis propria and invaded the high-intensity of mesorectal fat; T4 staging-tumors manifested as the tumor invaded adjacent structures or organs. The metastatic lymph nodes were showed with irregular boundaries and mixed signals on T2WI. The tumor signals could be found in the extramural vascular on T1-weighted images (T1WI), accompanied by irregular distortion and expansion of the blood vessels. On T2WI, metastatic lymph nodes, extramural vascular invasion, and the distance between the residual tumor and the low-signal of mesorectal fascia was within 1 mm, indicated the positive circumferential margin. On T2WI, the distal margin of the tumor was located at the junction of hyperintense submucosa and iso-signal of tumor, the tip of the iso-signal puborectal muscle was the apex of the anorectal ring, and the lowest point of the iso-signal external sphincter was the anal margin. Conclusion MRI can provide reliable imaging information for preoperative staging, height measurement, and prognosis of rectal cancer, and it is helpful for early diagnosis and treatment of rectal cancer.
To achieve non-contact measurement of human heart rate and improve its accuracy, this paper proposes a method for measuring human heart rate based on multi-channel radar data fusion. The radar data were firstly extracted by human body position identification, phase extraction and unwinding, phase difference, band-pass filtering optimized by power spectrum entropy, and fast independent component analysis for each channel data. After overlaying and fusing the four-channel data, the heartbeat signal was separated using frost-optimized variational modal decomposition. Finally, a chirp Z-transform was introduced for heart rate estimation. After validation with 40 sets of data, the average root mean square error of the proposed method was 2.35 beats per minute, with an average error rate of 2.39%, a Pearson correlation coefficient of 0.97, a confidence interval of [–4.78, 4.78] beats per minute, and a consistency error of –0.04. The experimental results show that the proposed measurement method performs well in terms of accuracy, correlation, and consistency, enabling precise measurement of human heart rate.
Objective To propose a lightweight end-to-end neural network model for automated Korotkoff sound phase recognition and subsequent blood pressure (BP) measurement, aiming to improve measurement accuracy and population adaptability. Methods We developed a streamlined architecture integrating depthwise separable convolution (DSConv), multi-head attention (MHA), and bidirectional gated recurrent unit (BiGRU). The model directly processes Korotkoff sound time-series signals to identify auscultatory phases. Systolic BP (SBP) and diastolic BP (DBP) were determined using Phase Ⅰ and PhaseⅤdetections, respectively. Given the clinical relevance of phase Ⅳ for specific populations (e.g., children and pregnant women, denoted as DBPIV), BP values from this phase were also recorded. Results The study enrolled 106 volunteers with 70 males, 36 females at mean age of (40.0±12.0) years. The model achieved 94.25% phase recognition accuracy. Measurement errors were (0.1±2.5) mm Hg (SBP), (0.9±3.4) mm Hg (DBPIV), and (0.8±2.6) mm Hg (DBP). Conclusion Our method enables precise phase recognition and BP measurement, demonstrating potential for developing population-adaptive blood pressure monitoring systems.
Due to lack of the practical technique to measure the biomechanical properties of the ocular cornea in vivo, clinical ophthalmologists have some difficulties in understanding the deformation mechanism of the cornea under the action of physiological intraocular pressures. Using Young's theory analysis of the corneal deformation during applanation tonometry, the relation between the elasticity moduli of the cornea and the applanated corneal area and the measured and true intraocular pressures can be obtained. A new applanation technique has been developed for measuring the biomechanical properties of the ocular cornea tissue in vivo, which can simultaneously acquire the data of the applanation area and displacement of the corneal deformation as well as the exerted applanation force on the cornea. Experimental results on a rabbit's eyeball demonstrated that the present technique could be used to measure the elasticity moduli and creep properties of the ocular cornea nondestructively in vivo.
Most of the existing near-infrared noninvasive blood glucose detection models focus on the relationship between near-infrared absorbance and blood glucose concentration, but do not consider the impact of human physiological state on blood glucose concentration. In order to improve the performance of prediction model, particle swarm optimization (PSO) algorithm was used to train the structure paramters of back propagation (BP) neural network. Moreover, systolic blood pressure, pulse rate, body temperature and 1 550 nm absorbance were introduced as input variables of blood glucose concentration prediction model, and BP neural network was used as prediction model. In order to solve the problem that traditional BP neural network is easy to fall into local optimization, a hybrid model based on PSO-BP was introduced in this paper. The results showed that the prediction effect of PSO-BP model was better than that of traditional BP neural network. The prediction root mean square error and correlation coefficient of ten-fold cross-validation were 0.95 mmol/L and 0.74, respectively. The Clarke error grid analysis results showed that the proportion of model prediction results falling into region A was 84.39%, and the proportion falling into region B was 15.61%, which met the clinical requirements. The model can quickly measure the blood glucose concentration of the subject, and has relatively high accuracy.